Contents

 

  1. Introduction to knowledge-based intelligent systems

    2.

    1.1 Intelligent machines, or what machines can do

    1.2 The history of artificial intelligence, or from the ‘Dark Ages’ to knowledge-based systems

    1.3 Summary

    Questions for review

    References

     

  2. Rule-based expert systems

    3.

    2.1 Introduction, or what is knowledge?

    2.2 Rules as a knowledge representation technique

    2.3 The main players in the expert system development team

    2.4 Structure of a rule-based expert system

    2.5 Fundamental characteristics of an expert system

    2.6 Forward chaining and backward chaining inference techniques

    2.7 THERMOSTAT: a demonstration rule-based expert system

    2.8 Conflict resolution

    2.9 Advantages and disadvantages of rule-based expert systems

    2.10 Summary

    Questions for review

    References

     

  3. Uncertainty management in rule-based expert systems

    4.

    3.1 Introduction, or what is uncertainty?

    3.2 Basic probability theory

    3.3 Bayesian reasoning

    3.4 FORECAST: Bayesian accumulation of evidence

    3.5 Bias of the Bayesian mesod

    3.6 Certainty factors theory and evidential reasoning

    3.7 FORECAST: an application of certainty factors

    3.8 Comparison of Bayesian reasoning and certainty factors

    3.9 Summary

    Questions for review

    References

     

  4. Fuzzy expert systems

    5.

    4.1 Introduction, or what is fuzzy thinking?

    4.2 Fuzzy sets

    4.3 Linguistic variables and hedges

    4.4 Operations of fuzzy sets

    4.5 Fuzzy rules

    4.6 Fuzzy inference

    4.7 Building a fuzzy expert system

    4.8 Summary

    Questions for review

    References

    Bibliography

     

  5. Frame-based expert systems

    6.

    5.1 Introduction, or what is a frame?

    5.2 Frames as a knowledge representation technique

    5.3 Inference in frame-based experts

    5.4 Methods and demons

    5.5 Interaction of frames and rules

    5.6 Buy Smart: a frame-based expert system

    5.7 Summary

    Questions for review

    References

    Bibliography

     

  6. Artificial neural networks

    7.

    6.1 Introduction, or how the brain works

    6.2 The neuron as a simple computing element

    6.3 The perceptron

    6.4 Multilayer neural networks

    6.5 Accelerated learning in multilayer neural networks

    6.6 The Hopfield network

    6.7 Bidirectional associative memories

    6.8 Self-organising neural networks

    6.9 Summary

    Questions for review

    References

     

  7. Evolutionary computation

    8.

    7.1 Introduction, or can evolution be intelligent?

    7.2 Simulation of natural evolution

    7.3 Genetic algorithms

    7.4 Why genetic algorithms work

    7.5 Case study: maintenance scheduling with genetic algorithms

    7.6 Evolutionary strategies

    7.7 Genetic programming

    7.8 Summary

    Questions for review

    References

     

  8. Hybrid intelligent systems

    9.

    8.1 Introduction, or how to combine German mechanics with Italian love

    8.2 Neural expert systems

    8.3 Neuro-fuzzy systems

    8.4 ANFIS: Adaptive Neuro-Fuzy Inference System

    8.5 Evolutionary neural networks

    8.6 Fuzzy evolutionary systems

    8.7 Summary

    Questions for review

    References

     

  9. Knowledge engineering and data mining

    10.

    9.1 Introduction, or what is knowledge engineering?

    9.2 Will an expert system work for my problem?

    9.3 Will a fuzzy expert system work for my problem?

    9.4 Will neural network work for my problem?

    9.5 Data mining and knowledge discovery

    9.7 Summary

    Questions for review

    References